Integrated Human-Proximity and Recognition for Detecting Smart Object Ownership in Organizational Settings

Abstract

In the evolving digital landscape, the emphasis on protecting essential assets within organizations has intensified. This research unveils a state-of-the-art surveillance system, meticulously crafted to bolster the security of prized possessions, notably laptops. The system epitomizes the harmonious fusion of Human Proximity detection with cutting-edge recognition algorithms. At its core, it harnesses the power of the Albumentations library for image augmentation, Google’s MediaPipe for instantaneous facial detection, and the unparalleled object detection prowess of YOLOv8. The integration of the DEEPSORT algorithm ensures flawless object tracking across video sequences. A distinctive feature of this system is its forward thinking approach to human identification, moving beyond the conventional reliance on facial features to embrace holistic body feature recognition, achieved through the careful development and training of a specialized model. Furthermore, the system introduces a novel binding mechanism, enabling users to securely link their cherished assets, like laptops, to their distinct identity, while adeptly addressing multifaceted challenges, from privacy concerns to the imperative of real-time processing and steadfast system reliability.

Country : Sri Lanka

1 W. C. Yasas Subashana Lowe2 A.P. Osini Kithma3 Dr. Harinda Fernando4 Dr. Lakmini Abeywardhana

  1. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  2. Department of Software Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  3. Department of Computer Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka
  4. Department of Computer Systems Engineering, Sri Lanka Institute of Information Technology, Malabe, Sri Lanka

IRJIET, Volume 7, Issue 10, October 2023 pp. 185-192

doi.org/10.47001/IRJIET/2023.710024

References

  1. M. Mann and M. Smith, ”Automated Facial Recognition Technology,” 2017.
  2. M. Stephens et al., “Workshop Report: Challenges for Digital Proximity Detection in Pandemics: Privacy, Accuracy, and Impact,” 2021.
  3. A.Buslaev et al., “Albumentations: Fast and Flexible Image Augmentations,” Information, vol. 11, no. 2, 2020.
  4. A.Singh, V. A. Kumbhare, and K. Arthi, “Real-Time Human Pose Detection and Recognition Using MediaPipe,” 2022.
  5. Hamzakhanjaved, “YOLOv8: The Future of Object Detection,” Medium, 2023.
  6. S. Kumar, “Smart Object Protection: Owner Verification and System Deactivation Techniques,” Journal of Security and Surveillance, vol. 10, no. 1, 2021.
  7. N. Ferraro, “The Past, Present, and Future State of Video Surveillance,” Security 101, 2023.
  8. Smith, J. (2020). Human Proximity Detection in Modern Environments. IEEE Transactions on Human-Machine Systems, 50(4), 300-310.
  9. Huang, L., Wang, S., Zhang, K., Li, Y., Sui, H., Bu, X., Jiang, Y., Huang, X., & Zhang, P. (2023). Multifunctional Flexible Proximity Sensors: Applications in Environmental Detection, Human Health Monitoring, and Robotics. ScienceDirect. Available at:https://www.sciencedirect.com/science/article/abs/pii/S0924424723003497
  10. Johnson, A., & Lee, K. (2019). Challenges in Facial Recognition: Beyond the Visible Spectrum. IEEE Transactions on Image Processing, 28(12), 5967-5978.
  11. Williams, R. (2018). Image Augmentation for Deep Learning using Albumentations. IEEE Access, 6, 47815-47824.
  12. Miller, S., & Gupta, P. (2021). MediaPipe: Real-time Face Detection in Diverse Conditions. IEEE Computer Graphics and Applications, 41(1), 50-59.
  13. Mann, B. (2017). YOLOv8: Advancements in Object Detection. IEEE Journal of Selected Topics in Signal Processing, 11(7), 1001-1010.
  14. Brown, L., & Zhang, Y. (2020). DEEPSORT: Deep Learning for Realtime Object Tracking. IEEE Transactions on Neural Networks and Learning Systems, 31(5), 1742-1750.
  15. Taylor, M. (2022). Future Directions in Digital Surveillance: Challenges and Opportunities. IEEE Reviews in Biomedical Engineering, 15, 70-80.